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I'm not sure I appreciate that criticism of Cython. A general strategy in Cython is to use the annotation feature (cython -a) and visually inspect the degree of

by p4wnc6 11y ago

I'm not sure I appreciate that criticism of Cython. A general strategy in Cython is to use the annotation feature (cython -a) and visually inspect the degree of CPython API necessary for the lines of Cython source code. I've generally found this process to be really enlightening. Of course you can use that information to select portions of the code that don't need to be involving the CPython API, add typed contructs to those parts of the Cython source, and iterate. But you can also learn a lot about how CPython works ... for example how using the 'and' keyword can invoke a long chain of Python special functions with lots of type checking overhead. What this lets me do is to be extremely fine-grained about my optimizations, or conversely, to also see when some optimizations are not worth it because they don't help much but they do hurt the readability, or cause too much Python-to-Cython divergence as you put it. In a lot of cases, I prefer that this is left up to me to do, rather than if Cython had already made hand-mapped choices about which Python things compile to which C or machine code things. If taken to the limit, a Cython that did that would just become what PyPy is, except it would be ahead of time compiled instead of JIT compiled. But I do see the benefit of both approaches. Sometimes you don't want the burden of choosing your annotations to induce the desired compilation effect, and you don't want to allow for similar but not identical Cython source files to result in dramatically different C code, as can often happen currently.

jboy 11y ago
The preferred workflow that you've described seems to be a (more considered) form of the standard Cython workflow that I see described: (1) Write Python. (2) Compile with Cython. (3) Run compiled Cython, profile & review. (4) Consider what Cython annotations to make to code; make code changes. (5) Goto 2. The emphasis is always on iteration & incremental additions. Of course I practice iterative & incremental development, and of course I'll prototype a quick proof-of-concept implementation first (often in Python+Numpy) before profiling & algorithmic optimization. But the Cython workflow seems to me to add more iteration (of incremental Cython syntax additions) than is really necessary. When I'm working to implement some algorithm, I don't really want to iteratively learn how Cython or CPython implement various Python functions; I'd rather write my "proper version" code just once, properly the first time. So why not take it to the logical extreme and write it all in Cython-lang from the start? If we're writing for-keeps code in a language with Pythonic syntax & static types, I find Nim-lang a more expressive, more full-featured language (with features such as generics & type-safe Lisp-like macros, in particular; I note that Cython does support pointers & operator overloading) than Cython-lang in general-purpose uses, without being very different at all in simple uses. For example, there is an example `primes` function in the Cython tutorial: http://docs.cython.org/src/tutorial/cython_tutorial.html#primes http://docs.cython.org/src/tutorial/cython_tutorial.html#pri... Here is an equivalent implementation in Nim. As you can see, it's really almost identical in syntax: proc primes(kmax: int): seq[int] = var kmax = kmax var n, k, i: int var p: array[1000, int] result = @[] if kmax > 1000: kmax = 1000 k = 0 n = 2 while k < kmax: i = 0 while i < k and n mod p[i] != 0: i = i + 1 if i == k: p[k] = n k = k + 1 result.add(n) n = n + 1 return result All of this said, I understand that a great deal of this decision comes down to personal preference: Would you rather start with Python & then iteratively diverge? Would you rather start & stay in Nim? And I can also see the benefit of both approaches in different circumstances. :)
- Nrpf 11y ago
  Or you can use numba and with pure python syntax and get really fast.
- porker 11y ago
  > The emphasis is always on iteration & incremental additions. > All of this said, I understand that a great deal of this decision comes down to personal preference: Would you rather start with Python & then iteratively diverge? When doing scientific research, I'd prefer to start with Python and then iteratively diverge. The aim is to get my task done as quickly as possible (so I can go write those 15 journal articles due last Monday) and go just as far as is needed. It's the scientist/engineer vs programmer mindset difference. However, if the code was done and I knew I'd need to use it again, I like the idea of rewriting it from scratch in Nim - not that I or any scientific colleagues know Nim (only one has heard of it). Anyway, I look forward to keeping an eye on your work and maybe using it in the future :)
- andreasvc 11y ago
  If you're writing in Cython (not Python) from scratch you don't need to work any more iteratively than in any other language. You can start out with low level memory management with malloc and declare everything with C types. Granted, generics and macros are a nice advantage of Nim. What I think is the essential difference here is that Nim is higher level than Cython. So it seems to boil down to a matter of preference. However, when Python is not fast enough, Cython lets you go all the way to generating C code without garbage collection, inline assembly etc. With Nim there's yet another layer between Python and C.

by p4wnc6 11y ago

jboy 11y ago

The preferred workflow that you've described seems to be a (more considered) form of the standard Cython workflow that I see described: (1) Write Python. (2) Compile with Cython. (3) Run compiled Cython, profile & review. (4) Consider what Cython annotations to make to code; make code changes. (5) Goto 2. The emphasis is always on iteration & incremental additions. Of course I practice iterative & incremental development, and of course I'll prototype a quick proof-of-concept implementation first (often in Python+Numpy) before profiling & algorithmic optimization. But the Cython workflow seems to me to add more iteration (of incremental Cython syntax additions) than is really necessary. When I'm working to implement some algorithm, I don't really want to iteratively learn how Cython or CPython implement various Python functions; I'd rather write my "proper version" code just once, properly the first time. So why not take it to the logical extreme and write it all in Cython-lang from the start? If we're writing for-keeps code in a language with Pythonic syntax & static types, I find Nim-lang a more expressive, more full-featured language (with features such as generics & type-safe Lisp-like macros, in particular; I note that Cython does support pointers & operator overloading) than Cython-lang in general-purpose uses, without being very different at all in simple uses. For example, there is an example `primes` function in the Cython tutorial: http://docs.cython.org/src/tutorial/cython_tutorial.html#primes http://docs.cython.org/src/tutorial/cython_tutorial.html#pri... Here is an equivalent implementation in Nim. As you can see, it's really almost identical in syntax: proc primes(kmax: int): seq[int] = var kmax = kmax var n, k, i: int var p: array[1000, int] result = @[] if kmax > 1000: kmax = 1000 k = 0 n = 2 while k < kmax: i = 0 while i < k and n mod p[i] != 0: i = i + 1 if i == k: p[k] = n k = k + 1 result.add(n) n = n + 1 return result All of this said, I understand that a great deal of this decision comes down to personal preference: Would you rather start with Python & then iteratively diverge? Would you rather start & stay in Nim? And I can also see the benefit of both approaches in different circumstances. :)

Nrpf 11y ago
Or you can use numba and with pure python syntax and get really fast.
porker 11y ago
> The emphasis is always on iteration & incremental additions. > All of this said, I understand that a great deal of this decision comes down to personal preference: Would you rather start with Python & then iteratively diverge? When doing scientific research, I'd prefer to start with Python and then iteratively diverge. The aim is to get my task done as quickly as possible (so I can go write those 15 journal articles due last Monday) and go just as far as is needed. It's the scientist/engineer vs programmer mindset difference. However, if the code was done and I knew I'd need to use it again, I like the idea of rewriting it from scratch in Nim - not that I or any scientific colleagues know Nim (only one has heard of it). Anyway, I look forward to keeping an eye on your work and maybe using it in the future :)
andreasvc 11y ago
If you're writing in Cython (not Python) from scratch you don't need to work any more iteratively than in any other language. You can start out with low level memory management with malloc and declare everything with C types. Granted, generics and macros are a nice advantage of Nim. What I think is the essential difference here is that Nim is higher level than Cython. So it seems to boil down to a matter of preference. However, when Python is not fast enough, Cython lets you go all the way to generating C code without garbage collection, inline assembly etc. With Nim there's yet another layer between Python and C.